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Exotic Spices, Food and Technology
26

Tonight, after a late talk in a hotel on a day conference on my software business , I meet up with an old friend I have not seen for 8 years and that we wanted to catch up on everything that has happened. It was rather late and We were in an area of London that we had little visibility on what is good and what is restaurants are still open within walking distance. I had my iPad and it came to our rescue and I found TripAdvisor not bad and found a place close with not so good reviews. We still went there and found the food to be fantastic. I am not absolutely sure any more about how good the reviews are on TripAdvisor. This negative review was probably done by a competitor trying to discredit the restaurant.

I wanted really to try out Ness - but sadly it was not available in UK. Ness is a personal search engine catering to restaurants. And the reviews in US say that it is good. Whereas most food and restaurant recommendation apps focus on ratings from the general population, Ness is tailored around social as well as your own tastes. Ness seems to have it nailed thanks to a lot of data they’ve already been pulling in before the app even launched.

Ness has a hook into Facebook, Foursquare, and Twitter (as well as a few other, smaller sources) They track the restaurants they focus on and check what people tweeting about liking them or dislikes? Are users checking in there a lot? They later analyse the data to determine what restaurants Ness should show to you within the app.

As soon as you install the app Ness wants to take its first set of data from you. Asking you ten places you’ve been. This helps calibrate your “Likeness” score, the key to the recommendations. Being honest is the key as these Likeness scores show up as percentage likelihood that you’ll like the restaurant being shown. This alongside proximity and the social data determine what restaurants are shown to you.

The app is also a search engine in the more traditional sense allowing you to easily search for different types of food, whether they’re broad or specific. And you can choose to search in other cities. Or you can alter the search settings to restrict a search by price and weed out bigger chains, for example.

When you find a place you like, you can drill deeper to get hours of operation, phone numbers, a website, etc. You can also save places to come back to later. And you can share restaurants with friends with the click of a button.

The app apparently from the reviews already out there, is easy to use and the accuracy of the data. I was constantly finding the Likeness ratings were roughly correct for what my actual rating would be for restaurants I’ve been to. And the system will obviously get better with time.

Remember that restaurant recommendations are just step one of what Ness hopes to offer. The next steps are to point the Likeness engine towards shopping, music, nightlife, and entertainment. All of that could make for one killer, subjective mobile search engine.


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